Quantitative Histomorphometric Features of Prostate Cancer Predict Patients Who Biochemically Recur Following Prostatectomy. Lab Invest 2023 Dec;103(12):100269
Date
10/29/2023Pubmed ID
37898290Pubmed Central ID
PMC10872376DOI
10.1016/j.labinv.2023.100269Scopus ID
2-s2.0-85181176849 (requires institutional sign-in at Scopus site) 1 CitationAbstract
Prostate cancer is the most commonly diagnosed cancer in men, accounting for 27% of the new male cancer diagnoses in 2022. If organ-confined, removal of the prostate through radical prostatectomy is considered curative; however, distant metastases may occur, resulting in a poor patient prognosis. This study sought to determine whether quantitative pathomic features of prostate cancer differ in patients who biochemically experience biological recurrence after surgery. Whole-mount prostate histology from 78 patients was analyzed for this study. In total, 614 slides were hematoxylin and eosin stained and digitized to produce whole slide images (WSI). Regions of differing Gleason patterns were digitally annotated by a genitourinary fellowship-trained pathologist, and high-resolution tiles were extracted from each annotated region of interest for further analysis. Individual glands within the prostate were identified using automated image processing algorithms, and histomorphometric features were calculated on a per-tile basis and across WSI and averaged by patients. Tiles were organized into cancer and benign tissues. Logistic regression models were fit to assess the predictive value of the calculated pathomic features across tile groups and WSI; additionally, models using clinical information were used for comparisons. Logistic regression classified each pathomic feature model at accuracies >80% with areas under the curve of 0.82, 0.76, 0.75, and 0.72 for all tiles, cancer only, noncancer only, and across WSI. This was comparable with standard clinical information, Gleason Grade Groups, and CAPRA score, which achieved similar accuracies but areas under the curve of 0.80, 0.77, and 0.70, respectively. This study demonstrates that the use of quantitative pathomic features calculated from digital histology of prostate cancer may provide clinicians with additional information beyond the traditional qualitative pathologist assessment. Further research is warranted to determine possible inclusion in treatment guidance.
Author List
Duenweg SR, Brehler M, Lowman AK, Bobholz SA, Kyereme F, Winiarz A, Nath B, Iczkowski KA, Jacobsohn KM, LaViolette PSAuthor
Peter LaViolette PhD Professor in the Radiology department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
HumansImage Processing, Computer-Assisted
Male
Neoplasm Grading
Prostate
Prostatectomy
Prostatic Neoplasms
